Abstract
The drawbacks that keep the standard ART1 paradigm from being a truly effective technique for optimizing the machine-part matrix were analyzed, and two changes to the standard ART1 paradigm were proposed. The first change involved pre-processing by fuzzy C-MEANS so as to promote classification precision; the second change was to modify the vector memory pattern to avoid too sparse representation vectors. The modified solution above-mentioned overcomes the shortcomings and makes the standard ART1 paradigm become a new effective method which can be used in real manufacturing cells design. A new algorithm chart was described. Simulation based on the standard of similarity coefficient was done in the platform of MATLAB and asserted its better results compared with the former researches. Finally, an engineering application was given in this way.
| Original language | English |
|---|---|
| Pages (from-to) | 1040-1043 |
| Number of pages | 4 |
| Journal | Zhongguo Jixie Gongcheng/China Mechanical Engineering |
| Volume | 17 |
| Issue number | 10 |
| State | Published - 25 May 2006 |
Keywords
- ART1 neural network
- Grouping efficiency
- Manufacturing cell
- Part family
- Similarity coefficient